Systems and methods for unsupervised neologism normalization of electronic content using embedding space mapping

ABSTRACT

Systems and methods are disclosed for utilizing a comment moderation bot for detecting and normalizing neologisms in social media. One method comprises transmitting, by a neologism normalization system, a comment moderation bot for detecting neologisms on an online platform maintained by one or more publisher systems. The comment moderation bot may aggregate data related to user comments and transmit the aggregated data to the neologism normalization system for further processing. The neologism normalization system implements unsupervised machine learning models for detecting neologisms in the aggregated data through tokenization and filtering; and normalizing the neologisms through similarity analysis and lattice decoding.

TECHNICAL FIELD

The present disclosure relates to normalizing neologisms and, moreparticularly, to systems and methods for utilizing a comment moderationbot for detecting and normalizing neologisms in social media.

BACKGROUND

Linguistic evolution and word coinage are naturally occurring phenomenain languages. The recent social media outbreak, however, has expeditedsuch processes through its informal social nature, and mainly writtencontent. One aspect of this change is the increasing use of neologisms.Neologisms are relatively recent terms that are used widely, and may bein the process of entering common use, but have not yet been fullyaccepted into mainstream language. Neologisms are rarely found intraditional dictionaries or language lexica. And they usually sharesemantic, lexical, and phonetic similarities to some relevant canonicalforms. They are also often, but not necessarily, generated throughcoinage of two different words into single entity. Examples include theword burkini, which is coined from the words burka and bikini. Theburkini has its own individual meaning that cannot be entailed by burkaor bikini alone.

Social media streams are noisy by nature, as such the process ofneologism normalization extends to both intentional and accidentalnon-standard content. Social media lexical phenomena are also evolvingrapidly, beyond the scope of traditional dictionary extension processes.Moreover, neologisms are usually related to certain timeframes, as such,the use of certain terms might decline at some point without enteringcommon linguistic lexica. The semantics of these terms might also shift,given updates to the contexts in which they are used. The means fordetecting neologisms through machine learning should be dynamic andrelatively inexpensive. Therefore, supervised approaches fall short,since resources for such tasks are expensive to develop and update tomatch current neologisms or neologisms coinage patterns.

Accordingly, solutions are needed for inexpensively detecting andnormalizing neologisms identified in online platforms. Thus, the presentdisclosure is directed to electronically detecting and normalizingneologisms without explicit supervised resources.

SUMMARY OF THE DISCLOSURE

Embodiments of the present disclosure include systems and methods forunsupervised neologism normalization using embedding space mapping.

According to certain embodiments, computer-implemented methods aredisclosed for unsupervised neologism normalization. One method includesdetecting one or more user generated comments on a publisher platform;tokenizing the one or more user generated comments; filtering the one ormore user generated comments against known lexemes stored in a databaseand removing the known lexemes from further analysis; selecting alanguage and auditing the one or more user generated comments forlexemes in order to identify and remove foreign lexemes from furtheranalysis; generating a list of remaining lexemes as a result of thefiltered and audited one or more user generated comments; identifyingsub-lexemes from the list of remaining lexemes; normalizing the list ofremaining lexemes through lattice decoding; and storing the normalizedlist of remaining lexemes in a neologism database.

According to certain embodiments, systems for unsupervised neologismnormalization. One system includes a processor configured to execute theinstructions to perform a method including: detecting one or more usergenerated comments on a publisher platform; tokenizing the one or moreuser generated comments; filtering the one or more user generatedcomments against known lexemes stored in a database and removing theknown lexemes from further analysis; selecting a language and auditingthe one or more user generated comments for lexemes in order to identifyand remove foreign lexemes from further analysis; generating a list ofremaining lexemes as a result of the filtered and audited one or moreuser generated comments; identifying sub-lexemes from the list ofremaining lexemes; normalizing the list of remaining lexemes throughlattice decoding; and storing the normalized list of remaining lexemesin a neologism database.

According to certain embodiments, non-transitory computer readablemedium for unsupervised neologism normalization. One non-transitorycomputer readable medium includes: a processor configured to execute theinstructions to perform a method including: detecting one or more usergenerated comments on a publisher platform; tokenizing the one or moreuser generated comments; filtering the one or more user generatedcomments against known lexemes stored in a database and removing theknown lexemes from further analysis; selecting a language and auditingthe one or more user generated comments for lexemes in order to identifyand remove foreign lexemes from further analysis; generating a list ofremaining lexemes as a result of the filtered and audited one or moreuser generated comments; identifying sub-lexemes from the list ofremaining lexemes; normalizing the list of remaining lexemes throughlattice decoding; and storing the normalized list of remaining lexemesin a neologism database.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 depicts a flow diagram of an exemplary method of detecting andnormalizing neologisms identified on an online platform.

FIG. 2 depicts a block diagram of an exemplary method of a neologismnormalization system detecting and normalizing neologisms identified onan online platform.

FIG. 3 depicts a schematic diagram of an exemplary method of detectingand normalizing neologisms identified on an online platform.

DETAILED DESCRIPTION OF EMBODIMENTS

While principles of the present disclosure are described herein withreference to illustrative embodiments for particular applications, itshould be understood that the disclosure is not limited thereto. Thosehaving ordinary skill in the art and access to the teachings providedherein, will recognize that the features illustrated or described withrespect to one embodiment, may be combined with the features of anotherembodiment. Therefore, additional modifications, applications,embodiments, and substitution of equivalents, all fall within the scopeof the embodiments described herein. Accordingly, the invention is notto be considered as limited by the foregoing description. Variousnon-limiting embodiments of the present disclosure will now be describedto provide an overall understanding of the principles of the structure,function, and use of system and method for unsupervised neologism usingembedding space mapping.

As described above, there is a need in the field of neologismnormalization for unsupervised processes for detecting and normalizingneologisms identified in online platforms. The past contributions forautomatic neologism handling, whether for detection or normalization,are relatively scarce. Existing neologisms detection approaches relysolely on exclusions lists of canonical or accepted lexemes to filterplausible neologisms or utilize additional filters like eliminatinglexemes with spelling errors or named entities, to further reduce theset of detected plausible neologisms. Other modern neologismnormalization techniques implement supervised machine learning models,which are inefficient because they require relatively large datasets andare expensive to maintain.

Therefore, automated methods for handling neologisms are important fornatural language understanding and normalization, especially forsocially generated content. Accordingly, the present disclosure isdirected to an unsupervised approach into detecting neologisms andnon-standard words, and then normalizing those detected neologisms andnon-standard words without the need of explicit supervised resources. Inparticular, unsupervised normalization machine learning models areideal. Accordingly, the following embodiments outline unsupervisedmachine learning models that implement instructions for analyzingdistributed lexemes embeddings captured from online content, where theembeddings are used to capture the notion of contextual similaritybetween canonical and noisy lexemes, along with other metrics andimplement phrase-based modeling, using existing phrase corpora.

In one embodiment, the initial step of neologism detection for aneologism normalization system transmits a comment moderation bot fordetecting neologisms on an online platform maintained by one or morepublisher systems. For example, here, a comment moderation bot mayaggregate text posted on the landing page of webpage and transmit theaggregated text data packets back to the neologism normalization systemfor further analysis.

In one embodiment, the analysis may further comprise: tokenization ofthe aggregated text data wherein code may be executed for implementingwhite space splits, identification of Uniform Resource Locators, andspecific punctuation patterns; named entities removal, wherein analgorithm analyzes the received aggregated text data packets andidentifies relevant nouns (e.g., people, places, and organizations) thatare mentioned in the text and further eliminates the named entities fromthe list; non-language (e.g., English) content removal, wherein code isexecuted for identifying and eliminating foreign (e.g., non-English)lexemes (e.g., phrases, tokens, and words); social media jargon removal,wherein code is executed for accessing social media platform specificglossaries and identifying and removing known social media jargon; andthe identification of spelling errors and the potential removal thereof.

The analysis of the received aggregated text data packets mayadditionally include a neologism normalization process, wherein theunsupervised machine learning model receives an input of corpora,mathematically detects similarities between lexemes (taking intoconsideration the context of the lexemes) corresponding to the corporaand outputs a feature vector (i.e., lexeme embedding) assigned to eachlexeme. The unsupervised machine learning model then uses the lexemeembeddings to gain a broader understanding of normalization lexicons andfurther uses the lexicons to obtain scored normalization candidates foreach neologism that is ultimately identified.

The analysis of the received aggregated text data packets may furtherinclude computer-implemented instructions for lexicon normalization andlattice decoding using a list of canonical word forms as canonicalcandidates. This list of canonical forms can be obtained for aparticular language, (e.g., from traditional English language lexicalike the Gigaword corpus). For each canonical candidate, theunsupervised machine learning model may retrieve the N nearest neighborsfrom the lexeme embeddings. This effectively functions as a reversednormalization lexicon, where the canonical candidates are mapped to thepotential neologisms. The canonical forms may be scored using severalsimilarity metrics and the unsupervised machine learning model may thenutilize reverse mapping techniques to get a list of scored canonicalcandidates for each neologism. Neologisms are expected to sharesemantic, lexical, and phonetic similarity with their canonicalcounterparts. As such, as will be described with respect to FIG. 2 ,normalization may involve the calculation of semantic, lexical, andphonetic similarity scores. In one embodiment, the following algorithmsreflect examples of equations suitable for the calculation of suchsimilarity domains:

One example of a semantic similarity score may be calculated usingcosine distance, as follows:

$C_{OS} = \frac{{\sum}_{i = 1}^{D}u_{i} \times v_{i}}{\sqrt{{\sum}_{i = 1}^{D}( u_{i} )^{2} \times {\sum}_{i = 1}^{D}( v_{i} )^{2}}}$

One example of a lexical similarity score may be calculated, as follows:

${L_{EX}( {S_{1},S_{2}} )} = \frac{LCS{R( {S_{1},S_{2}} )}}{E{D( {S_{1},S_{2}} )}}$${LCS{R( {S_{1},S_{2}} )}} = \frac{LC{S( {S_{1},S_{2}} )}}{{Max}( {{❘S_{1}❘},{❘S_{2}❘}} )}$

where LCSR refers to the Longest Common Subsequence Ratio, and LCSrefers to Longest Common Subsequence between the strings that areanalyzed. ED is the edit distance.

One example of a phonetic similarity score may be calculated, asfollows:

$P_{HON} = {1 - \frac{{ED}( {{{mPhon}( S_{1} )},{{mPhon}( S_{2} )}} )}{{Max}( {{❘S_{1}❘},{❘S_{2}❘}} )}}$

where mPhon is the Metaphone score. In one embodiment, an unsupervisedmachine learning language model may be used to further control thefluency of the normalized output. For example, the machine learningmodel may use statistical or neural language models. The unsupervisedmachine learning model may then decode the optimal path based on thesimilarity scores and the language model probabilities, and encode thesentence, along with the various normalization candidates, in the HTKStandard Lattice Format. The lattice-tool toolkit may then be utilizedto decode a set of potential paths using a Viterbi decoding algorithm.

Without limitation, the unsupervised machine learning model is notlimited to identifying individual lexemes. In one embodiment of thepresent disclosure, the unsupervised machine learning model implementsinstructions for identifying phrases and sub-lexeme units. Detecting andidentifying phrases may involve one or more methods outlined above andadditionally use a data-driven approach for learning the phrases withincorpora. For example, phrase candidates with scores above a certainthreshold are used as phrases. The lexemes within the phrases may beseparated by a delimiter and considered as a single lexeme-like entityfor any consequent analysis and further filtered for punctuationsequences, URLs, and social media jargon.

The neologism normalization system may additionally identify sub-lexemesthrough a data compression technique, which allows the neologismnormalization system to utilize less computer-based memory. Here, thecompression technique iteratively replaces the most frequent pair ofbytes in a sequence with a single, unused byte and instead of mergingfrequent pairs of bytes, characters or character sequences are merged.The neologism normalization system initializes a symbol vocabulary witha character vocabulary, and represent each lexeme as a sequence ofcharacters, plus a special end-of-lexeme symbol which allows theneologism normalization system to restore the original tokenizationafter translation. The neologism normalization system may iterativelycount all symbol pairs and replace each occurrence of the most frequentpair for example, (‘A’, ‘M’) with a new symbol ‘AM’. Each mergeoperation produces a new symbol which represents a character n-gram.Frequent character n-grams (or whole lexemes) are eventually merged intoa single symbol. The final symbol vocabulary size is equal to the sizeof the initial vocabulary, plus the number of merge operations. Theneologism normalization system can analyze an extracted text, whereineach lexeme is weighted by its frequency. Here, unlike previous methods,the neologism normalization system ensures symbol sequences are stillinterpretable as sub-lexeme units, which the network can generalize totranslate and produce new lexemes (unseen at training time) on the basisof these sub-lexeme units. This methodology provides a significantimprovement in the field of neologism normalization and improves theefficiency of storing data.

An important aspect to consider when combining the representations is tomaintain the text's distributional properties. The neologismnormalization system may combine the different lexeme representationsthrough a random distribution, by having the choice to switch to acertain representation, for each lexeme, dictated through a randomvariable. That is, for a given sentence S in a corpus, and for eachlexeme E S, the resulting representation based on the distribution ismanaged by the control variable c=rand(α), where α∈{0, 1, 2, . . . }.Each value represents a different representation level. This process isrepeated for all the lexemes of each sentence k different times, so weend up with k different copies of the sentence, each having a randomlyselected representation for all of its lexemes.

FIG. 1 depicts a flow diagram of an exemplary method of detecting andnormalizing neologisms identified on an online platform. As shown inFIG. 1 , method 100 comprises a step in which a neologism normalizationsystem implements a comment moderation bot for detecting one or moreuser generated comments on a publisher platform (Step 102). For example,a comment moderation bot may aggregate content data (e.g., textual data)recited on an online platform (e.g., a webpage, social media timeline,audio/video/textual/augmented reality post) and transmit the aggregatedcontent data to the neologism normalization system.

The neologism normalization system may then tokenize the receivedtextual data (e.g., one or more user generated comments) received in thetransmission from the comment moderation bot (Step 104). The neologismnormalization system may further filter the textual data against knownlexemes stored in a database and remove the known lexemes received inthe textual data from further analysis (Step 106). The neologismnormalization system may select a language (e.g., English) and audit thetextual data for lexemes in order to identify and remove foreign (e.g.,non-English) lexemes from further analysis (Step 108). The neologismnormalization system may generate a list of remaining lexemes as aresult of the filtered and audited textual data (Step 110). Theneologism normalization system may also identify sub-lexemes from thelist of remaining lexemes (Step 112). The neologism normalization systemmay then normalize the list of remaining lexemes through latticedecoding (Step 114) and store the normalized list of remaining lexemesin a neologism database (Step 116).

FIG. 2 depicts a block diagram of an exemplary method of a neologismnormalization system 200 configured for detecting and normalizingneologisms identified on an online electronic content platform. As shownin FIG. 2 , in one embodiment, a neologism normalization system 200 maycomprise a comment storage platform 204, a content pre-processing module206, a detection module 208, and a normalization module 214. As shown,neologism normalization system 200 may also access or retrieve usercomments 202, transmit or store normalized comments 224, and access,store, and/or maintain a database of canonical words and phrases 222.

In one embodiment, user-generated comments (or alternatively content andtextual data) 202 may be aggregated from any type of online electroniccontent platform and stored on a comment storage platform 204, forexample, by a comment moderation bot. The user-generated comments maythen be received by the content pre-processing module 206 for analysisof the type, volume, and origin of the user-generated comments. Thedetection module 208 may then receive the user-generated comments andimplement instructions for tokenization at a tokenization module 210 andfiltering at a filtering module 212 for filtering of the user comments.The tokenization module 210 may consist of executing code forimplementing white space splits, and identifying Uniform ResourceLocators and specific punctuation patterns. The filtering module 212 mayanalyze the received user-generated comments and perform techniques fornamed entities removal, wherein an algorithm analyzes the receivedaggregated text data packets and identifies relevant nouns (e.g.,people, places, and organizations) that are mentioned in the text andfurther eliminates the named entities from the list; non-language (e.g.,English) content removal, wherein code is executed for identifying andeliminating foreign (e.g., non-English) lexemes (e.g., phrases, tokensand words); social media jargon removal, wherein code is executed foraccessing social media platform specific glossaries and identifying andremoving known social media jargon; and the identification of spellingerrors and the potential removal thereof.

In one embodiment, the user-generated comments that remain aftertokenization at the tokenization module 210 and filtering at thefiltering module 212 may then be received at the normalization module214. Upon receipt of the user-generated comments, the normalizationmodule 214 may implement instructions for subword unit analysis 220, foridentifying phrases and sub-lexeme units. Detecting and identifyingphrases may involve one or more methods outlined above and additionallyuse a data-driven approach for learning the phrases within corpora.Phrase candidates with scores above a certain threshold may be used asphrases.

The lexemes within the phrases may be separated by a delimiter andconsidered as a single lexeme-like entity for any consequent analysis bythe similarity module 218. The similarity module 218 may receive a listof canonical forms from a canonical words and phrases database 222 for aparticular language, (e.g. traditional English language lexica), likethe Gigaword corpus. For example, as described above, neologisms may beexpected to share semantic, lexical, and phonetic similarity with theircanonical counterparts. As such, similarity module 218 may implement thefollowing algorithms, including: semantic similarity, lexical similarityand phonetic similarity, for evaluating canonical words & phrasesmatching context with that of the identified neologisms. Thus,similarity module 218 may analyze the user comments against thecanonical words and phrases in order to establish similarity metrics.

In one embodiment, the result(s) of the analysis by the similaritymodule 218 may then be received by the lattice decoding module 216,wherein for each canonical candidate, an unsupervised machine learningmodel may retrieve the N nearest neighbors from the neural lexemeembeddings. This effectively functions as a reversed normalizationlexicon, where the canonical candidates are mapped to the potentialneologisms. The canonical forms may be scored using several similaritymetrics and the unsupervised machine learning model may then utilizereverse mapping techniques to get a list of scored canonical candidatesfor each neologism. The lattice decoding module 216 may generate a listof neologisms wherein the neologisms are further categorized asnormalized comments 224.

FIG. 3 depicts a schematic diagram of an exemplary method of detectingand normalizing neologisms identified on an online electronic contentplatform, according to an exemplary embodiment of the presentdisclosure. As shown in FIG. 3 , the environment 300 may include atleast one neologism normalization system, one or more publisher systems310, and user devices 312, which may include laptop and desktopcomputers, Internet-enabled mobile devices, or any Internet-enableddevice. An electronic network 308 may be, for example, the Internet, butmay also be or comprise a Local Area Network (LAN), Wide Area Network(WAN), Wireless Local Area Network (WLAN), Metropolitan Area Network(MAN), and/or Storage Area Network (SAN), etc. A website (or a socialmedia application/platform) may be hosted by a publisher system 310 sothat it is made accessible to one or more user devices 312 and theneologism normalization system 302.

The neologism normalization system 302, which may be any type ofdistributed processing web servers and/or content delivery network(CDN), may be configured to monitor and/or intercept content data (e.g.audio, video, or textual data, and code thereof), through a commentmoderation bot. The neologism normalization system 302 may comprise ofone or more servers 304 and databases 306. Similarly, the one or morepublisher systems 310 may comprise one or more web servers,communications servers, and databases. Further, steps of the methodsdepicted in FIGS. 1 and 2 may be practiced on any combination of thedevices depicted in FIG. 3 .

The aforementioned systems and methods may be implemented via anysuitable system infrastructure. The following discussion provides abrief, general description of a suitable computing environment in whichthe present disclosure may be implemented. Although not illustrated,aspects of the present disclosure are described in the context ofcomputer-executable instructions, such as routines executed by a dataprocessing device, e.g., a server computer, wireless device, and/orpersonal computer. Those skilled in the relevant art will appreciatethat aspects of the present disclosure can be practiced with othercommunications, data processing, or computer system configurations,including: Internet appliances, hand-held devices (including personaldigital assistants (“PDAs”)), wearable computers (e.g. smart watches,smart jewelry, smart medical devices, smart medical aids), all manner ofcellular or mobile phones (including Voice over IP (“VoIP”) phones),dumb terminals, media players, gaming devices, multi-processor systems,microprocessor-based or programmable consumer electronics, set-topboxes, network PCs, mini-computers, mainframe computers, and the like.Indeed, the terms “computer,” “server,” and the like, are generally usedinterchangeably herein, and refer to any of the above devices andsystems, as well as any data processor. However, some machine learning,deep learning and neural network environments may require more robustprocessing units; for example, an Application Specific IntegratedCircuit (ASIC), Tensor Processing Unit (TPU) which may be assembled with16 GB of high bandwidth memory and may be capable of delivering over 180teraflop performance; or a Graphics Processing Unit (GPU). Some or allof the database(s) described herein, may comprise a cache, a buffer, arelational database, an active database, a matrix, a self-referentialdatabase, a table, a non-relational No-SQL system, an array, a flatfile, a documented-oriented storage system, and the like.

Aspects of the present disclosure may be embodied in a special purposecomputer and/or data processor that is specifically programmed,configured, and/or constructed to perform one or more of thecomputer-executable instructions explained in detail herein. Whileaspects of the present disclosure, such as certain functions, aredescribed as being performed exclusively on a single device, the presentdisclosure may also be practiced in distributed environments wherefunctions or modules are shared among disparate processing devices,which are linked through a communications network, such as a Local AreaNetwork (“LAN”), Wide Area Network (“WAN”), and/or the Internet. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

Aspects of the present disclosure may be stored and/or distributed onnon-transitory computer-readable media, including magnetically oroptically readable computer discs, hard-wired or preprogrammed chips(e.g., EEPROM semiconductor chips), nanotechnology memory, biologicalmemory, or other data storage media. Alternatively, computer implementedinstructions, data structures, screen displays, and other data underaspects of the present disclosure may be distributed over the Internetand/or over other networks (including wireless networks), on apropagated signal on a propagation medium (e.g., an electromagneticwave(s), a sound wave, etc.) over a period of time, and/or they may beprovided on any analog or digital network (packet switched, circuitswitched, or other scheme).

The systems, apparatuses, devices, and methods disclosed herein aredescribed in detail by way of examples and with reference to thefigures. The examples discussed herein are examples only and areprovided to assist in the explanation of the apparatuses, devices,systems, and methods described herein. None of the features orcomponents shown in the drawings or discussed below should be taken asmandatory for any specific implementation of any of these theapparatuses, devices, systems or methods unless specifically designatedas mandatory. For ease of reading and clarity, certain components,modules, or methods may be described solely in connection with aspecific figure. In this disclosure, any identification of specifictechniques, arrangements, etc. are either related to a specific examplepresented or are merely a general description of such a technique,arrangement, etc. Identifications of specific details or examples arenot intended to be, and should not be, construed as mandatory orlimiting unless specifically designated as such. Any failure tospecifically describe a combination or sub-combination of componentsshould not be understood as an indication that any combination orsub-combination is not possible. It will be appreciated thatmodifications to disclosed and described examples, arrangements,configurations, components, elements, apparatuses, devices, systems,methods, etc. can be made and may be desired for a specific application.Also, for any methods described, regardless of whether the method isdescribed in conjunction with a flow diagram, it should be understoodthat unless otherwise specified or required by context, any explicit orimplicit ordering of steps performed in the execution of a method doesnot imply that those steps must be performed in the order presented butinstead may be performed in a different order or in parallel.

Reference throughout the specification to “various embodiments,” “someembodiments,” “one embodiment,” “some example embodiments,” “one exampleembodiment,” or “an embodiment” means that a particular feature,structure, or characteristic described in connection with any embodimentis included in at least one embodiment. Thus, appearances of the phrases“in various embodiments,” “in some embodiments,” “in one embodiment,”“some example embodiments,” “one example embodiment, or “in anembodiment” in places throughout the specification are not necessarilyall referring to the same embodiment. Furthermore, the particularfeatures, structures, or characteristics may be combined in any suitablemanner in one or more embodiments.

Throughout this disclosure, references to components or modulesgenerally refer to items that logically can be grouped together toperform a function or group of related functions. Like referencenumerals are generally intended to refer to the same or similarcomponents. Components and modules can be implemented in software,hardware, or a combination of software and hardware. The term “software”is used expansively to include not only executable code, for examplemachine-executable or machine-interpretable instructions, but also datastructures, data stores and computing instructions stored in anysuitable electronic format, including firmware, and embedded software.The terms “information” and “data” are used expansively and includes awide variety of electronic information, including executable code;content such as text, video data, and audio data, among others; andvarious codes or flags. The terms “information,” “data,” and “content”are sometimes used interchangeably when permitted by context.

It should be noted that although for clarity and to aid in understandingsome examples discussed herein might describe specific features orfunctions as part of a specific component or module, or as occurring ata specific layer of a computing device (for example, a hardware layer,operating system layer, or application layer), those features orfunctions may be implemented as part of a different component or moduleor operated at a different layer of a communication protocol stack.Those of ordinary skill in the art will recognize that the systems,apparatuses, devices, and methods described herein can be applied to, oreasily modified for use with, other types of equipment, can use otherarrangements of computing systems such as client-server distributedsystems, and can use other protocols, or operate at other layers incommunication protocol stacks, than are described.

It is intended that the specification and examples be considered asexemplary only, with a true scope and spirit of the disclosure beingindicated by the following claims.

1-20. (canceled)
 21. A system for neologism normalization, the systemcomprising: a data storage device storing instructions and a processorconfigured to execute the instructions for: detecting user generatedcomments on an electronic platform; removing known lexemes of the usergenerated comments from further analysis; normalizing the remaininglexemes through lattice decoding; and storing the normalized lexemes ina database.
 22. The system of claim 21 further comprising tokenizing theuser generated comments, wherein tokenizing includes executing code forimplementing one or more of: creating white space splits, identifyingUniform Resource Locators, or identifying specific punctuation patterns.23. The system of claim 21, wherein detecting one or more user generatedcomments on the electronic platform further comprises, scanning contentretrieving lexical data for analysis.
 24. The system of claim 21 furthercomprising: determining a type of content associated with the usergenerated comments.
 25. The system of claim 21, wherein removing knownlexemes of the user generated comments from further analysis furthercomprises identifying one or more of social media jargon, namedentities, spelling errors, and abbreviations, in the one or more usergenerated comments.
 26. The system of claim 21, wherein normalizing theremaining lexemes through lattice decoding further comprises: retrievinga list of known lexemes from a corpus and comparing the list of knownlexemes to the remaining lexemes and assigning a score to the lexemes inthe list of known lexemes based on similarity metrics.
 27. The system ofclaim 26, further comprising: wherein comparing the list of knownlexemes to the list of remaining lexemes further comprises comparing atype of lexemes in the list of known lexemes to the type of lexemes inthe list of lexemes; and wherein assigning a score to the lexemes in thelist of known lexemes comprises analyzing the lexemes in the list ofknown lexemes for semantic similarity, lexical similarity and phoneticsimilarity, to lexemes in the list of lexemes.
 28. Acomputer-implemented method for neologism normalization, thecomputer-implemented method comprising: detecting user generatedcomments on an electronic platform; removing known lexemes of the usergenerated comments from further analysis; normalizing the remaininglexemes through lattice decoding; and storing the normalized lexemes ina database.
 29. The computer-implemented method of claim 28 furthercomprising tokenizing the user generated comments, wherein tokenizingincludes executing code for implementing one or more of: creating whitespace splits, identifying Uniform Resource Locators, or identifyingspecific punctuation patterns.
 30. The computer-implemented method ofclaim 28, wherein detecting one or more user generated comments on theelectronic platform further comprises, scanning content retrievinglexical data for analysis.
 31. The computer-implemented method of claim28, further comprising: determining a type of content associated withthe user generated comments.
 32. The computer-implemented method ofclaim 28, wherein removing known lexemes of the user generated commentsfrom further analysis further comprises identifying one or more ofsocial media jargon, named entities, spelling errors, and abbreviations,in the one or more user generated comments.
 33. The computer-implementedmethod of claim 28, wherein normalizing the remaining lexemes throughlattice decoding further comprises: retrieving a list of known lexemesfrom a corpus and comparing the list of known lexemes to the remaininglexemes and assigning a score to the lexemes in the list of knownlexemes based on similarity metrics.
 34. The computer-implemented methodof claim 33, further comprising: wherein comparing the list of knownlexemes to the list of remaining lexemes further comprises comparing atype of lexemes in the list of known lexemes to the type of lexemes inthe list of lexemes; and wherein assigning a score to the lexemes in thelist of known lexemes comprises analyzing the lexemes in the list ofknown lexemes for semantic similarity, lexical similarity and phoneticsimilarity, to lexemes in the list of lexemes.
 35. A non-transitorycomputer readable medium storing instructions that, when executed by aprocessor, cause the processor to perform a method for neologismnormalization, comprising: detecting user generated comments on anelectronic platform; removing known lexemes of the user generatedcomments from further analysis; normalizing the remaining lexemesthrough lattice decoding; and storing the normalized lexemes in adatabase.
 36. The non-transitory computer readable medium of claim 35further comprising tokenizing the user generated comments, whereintokenizing includes executing code for implementing one or more of:creating white space splits, identifying Uniform Resource Locators, oridentifying specific punctuation patterns.
 37. The non-transitorycomputer readable medium of claim 35, wherein detecting one or more usergenerated comments on the electronic platform further comprises,scanning content retrieving lexical data for analysis.
 38. Thenon-transitory computer readable medium of claim 35, further comprising:determining a type of content associated with the user generatedcomments.
 39. The non-transitory computer readable medium of claim 35,wherein normalizing the remaining lexemes through lattice decodingfurther comprises: retrieving a list of known lexemes from a corpus andcomparing the list of known lexemes to the remaining lexemes andassigning a score to the lexemes in the list of known lexemes based onsimilarity metrics.
 40. The non-transitory computer readable medium ofclaim 39, further comprising: wherein comparing the list of knownlexemes to the list of remaining lexemes further comprises comparing atype of lexemes in the list of known lexemes to the type of lexemes inthe list of lexemes; and wherein assigning a score to the lexemes in thelist of known lexemes comprises analyzing the lexemes in the list ofknown lexemes for semantic similarity, lexical similarity and phoneticsimilarity, to lexemes in the list of lexemes.